Automatic-Speech-Recognitio

End-to-end automatic speech recognition system implemented in TensorFlow.

Content

Automatic Speech Recognition implemented in TensorFlow contains followings:

• Data Pre-processing
• Acoustic Modeling(RNN,LSTM,BRNN,BLSTM,etc.)
• CTC Decoding
• Evaluation
• Saving or Restoring Model
• Mini-batch Training
• Training with GPU or CPU with TensorFlow
• Keeping logging of epoch time and error rate in disk
• Support TIMIT and Libri

Usage

python train.py
	--mfcc_dir '/data/mfcc/'
	--label_dir '/data/label/'
	--keep False
	--save True
	--evaluation False
	--learning_rate 0.001
	--batch_size 32
	--num_feature 39
	--num_hidden 128
	--num_classes 28
	--save_dir '/src/save/'
	--restore_from '/src/save/'
	--model_checkpoint_path '/src/save/'

Instead of configuration in command line, you can also set the arguments above in train.py in practice.

Implementation Details

Data preprocessing

Automatic Speech Recognition is to transcribe a raw audio file into character sequences. Data preprocessing is to convert a raw audio file into feature vectors of several frames. Here, we first split each audio file by a 20ms hamming window with no overlap, and then calculate the 12 mel frequency ceptral coefficients appended by a energy variable for each frame. Based on this vector of length 13, we calculate the delta coefficients and delta-delta coefficients, totally 39 coefficients for each frame. Therefore, each audio file is splited to several frames by hamming window, and each frame is extracted to a feature vector of length 39.

In folder data/mfcc, each file is a feature matrix with size timeLength*39 of one audio file; in folder data/label, each file is a label vector according to the mfcc file.

If you want to set your own data preprocessing, you can edit calcmfcc.py or preprocess.py.

Acoustic Model

TODO

CTC Decoding

TODO

Evaluation

TODO

Future work

TODO